Applying causal discovery algorithm to find predictors for transformation process of wood combustion emission

Aerosols and their transformation process in atmosphere have significant effects on climate. Transformation process is a complex combination of physical and chemical reactions. Multiple oxidizing agents and other factors, such as radiation, affect the transformation process. Characterization of these factors and their strength is a problem, where advanced methods might help to gain more understanding.

In this work, we modeled transformation of wood combustion emission measured in the environmental chamber by using causal modeling (Pearl, 2009). The aim of the study was to use state-of-the-art causal discovery methods to search causal pathways between measured variables: precursors and particle products. The data used in the modelling are introduced in Tiitta et al. (2016).

In addition to wood combustion experiments, we simulated artificial datasets to understand abilities of the model. We wanted to evaluate the accuracy of our model to confirm the correct structure between variables and reproduce the measured transformation. This helps us to understand the model performance in real datasets.

We found that model could reproduce the measured evolution well. The structure between emission parts was not completely matching to prior assumption. Usually incorrect predictors in the modeled structure are highly correlated with correct causes.

 

References:

Pearl, J.: Causality : Models, Reasoning and Inference., Cambridge University Press., 2009.

Tiitta et al., Atmos. Chem. Phys., 16, 13251-13269, 2016.